{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OvBwRxvXhzpF"
   },
   "source": [
    "# Synthetic Dataset Generator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-2MSGQC8uwuA"
   },
   "source": [
    "## 0. Setup and sign in to Hugging Face"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 37639,
     "status": "ok",
     "timestamp": 1759633893217,
     "user": {
      "displayName": "thinsri@gmx.de",
      "userId": "18422164867366802681"
     },
     "user_tz": -120
    },
    "id": "ciBOYWb4uq9h",
    "outputId": "5b4a31a9-ea21-4e91-ebee-01a0e3c77030"
   },
   "outputs": [],
   "source": [
    "!pip install -q requests bitsandbytes==0.46.0 transformers==4.48.3 accelerate==1.3.0 openai"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "yDnr7d2607E5"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import requests\n",
    "import io\n",
    "import tempfile\n",
    "import torch\n",
    "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, TextStreamer, TextIteratorStreamer\n",
    "from google.colab import userdata\n",
    "from huggingface_hub import login\n",
    "from IPython.display import display, Markdown, update_display\n",
    "from threading import Thread\n",
    "from dotenv import load_dotenv\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "tZz5QoVJ1GTL"
   },
   "outputs": [],
   "source": [
    "hf_token = userdata.get('HF_TOKEN')\n",
    "login(hf_token, add_to_git_credential=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "96RmuUrnut9G"
   },
   "source": [
    "## 1. Code Prototyping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "5T_fMfFguzDM"
   },
   "outputs": [],
   "source": [
    "# Define the model name\n",
    "LLAMA = \"meta-llama/Meta-Llama-3.1-8B-Instruct\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "voGGr8V21P6-"
   },
   "outputs": [],
   "source": [
    "def load_model(model_name):\n",
    "  quant_config = BitsAndBytesConfig(\n",
    "      load_in_4bit=True,\n",
    "      bnb_4bit_use_double_quant=True,\n",
    "      bnb_4bit_quant_type=\"nf4\",\n",
    "      bnb_4bit_compute_dtype=torch.bfloat16\n",
    "  )\n",
    "  tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
    "  tokenizer.pad_token = tokenizer.eos_token\n",
    "  model = AutoModelForCausalLM.from_pretrained(model_name, quantization_config=quant_config, device_map=\"auto\")\n",
    "  return tokenizer, model\n",
    "\n",
    "def generate_stream_with_thread(messages, tokenizer, model):\n",
    "  inputs = tokenizer.apply_chat_template(messages, return_tensors=\"pt\", return_attention_mask=True).to(\"cuda\")\n",
    "  input_token_len = inputs[0].shape[-1] # Get the length of the input tokens\n",
    "  streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)\n",
    "\n",
    "  # Generate in separate thread\n",
    "  thread = Thread(target=model.generate, kwargs={\"inputs\": inputs, \"max_new_tokens\": 500, \"streamer\": streamer})\n",
    "  thread.start()\n",
    "\n",
    "  # Stream and optionally filter output\n",
    "  unwanted_patterns = [\"assistant\", \"<|\", '|>']\n",
    "  response = \"\"\n",
    "  for text in streamer:\n",
    "    if text.strip() in unwanted_patterns:\n",
    "      continue\n",
    "    else:\n",
    "      print(text, end=\"\")\n",
    "  thread.join()\n",
    "\n",
    "def generate_stream_with_thread_gradio(messages, tokenizer, model):\n",
    "  \"\"\"Same as generate_stream_with_thread but yield accumulated reply\"\"\"\n",
    "  inputs = tokenizer.apply_chat_template(messages, return_tensors=\"pt\", return_attention_mask=True).to(\"cuda\")\n",
    "  input_token_len = inputs[0].shape[-1] # Get the length of the input tokens\n",
    "  streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)\n",
    "\n",
    "  # Generate in separate thread\n",
    "  thread = Thread(target=model.generate, kwargs={\"inputs\": inputs, \"max_new_tokens\": 500, \"streamer\": streamer})\n",
    "  thread.start()\n",
    "\n",
    "  # Stream and optionally filter output\n",
    "  unwanted_patterns = [\"assistant\", \"<|\", '|>']\n",
    "  response = \"\"\n",
    "  for text in streamer:\n",
    "    if text.strip() in unwanted_patterns:\n",
    "      continue\n",
    "    else:\n",
    "      response += text\n",
    "      yield response\n",
    "  thread.join()\n",
    "\n",
    "def generate_answer(messages, tokenizer, model):\n",
    "  inputs = tokenizer.apply_chat_template(messages, return_tensors=\"pt\", return_attention_mask=True).to(\"cuda\")\n",
    "  input_token_len = inputs[0].shape[-1] # Get the length of the input tokens\n",
    "  outputs = model.generate(inputs, max_new_tokens=500)\n",
    "  decoded_output = tokenizer.decode(outputs[0][input_token_len:], skip_special_tokens=True, clean_up_tokenization_spaces=True)\n",
    "  return decoded_output.replace(\"assistant\\n\\n\", \"\")"
   ]
  },
  {
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     "base_uri": "https://localhost:8080/",
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    },
    "executionInfo": {
     "elapsed": 602984,
     "status": "ok",
     "timestamp": 1759634516171,
     "user": {
      "displayName": "thinsri@gmx.de",
      "userId": "18422164867366802681"
     },
     "user_tz": -120
    },
    "id": "8KT2cbPEBDdZ",
    "outputId": "bc2ca7a3-6191-4fef-83fb-b34926dec936"
   },
   "outputs": [],
   "source": [
    "# test basic functions\n",
    "tokenizer, model = load_model(LLAMA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5686,
     "status": "ok",
     "timestamp": 1759634552750,
     "user": {
      "displayName": "thinsri@gmx.de",
      "userId": "18422164867366802681"
     },
     "user_tz": -120
    },
    "id": "aa1WGIn8BYzy",
    "outputId": "207a2342-ce36-40e1-e2bc-d57dc599f323"
   },
   "outputs": [],
   "source": [
    "system_message = [{\"role\": \"system\", \"content\": \"You are a helpful assistant.\"}]\n",
    "user_prompt = \"Tell me 4 line poem.\"\n",
    "messages = system_message + [{\"role\": \"user\", \"content\": user_prompt}]\n",
    "generate_stream_with_thread(messages, tokenizer, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 8333,
     "status": "ok",
     "timestamp": 1759465246704,
     "user": {
      "displayName": "thinsri@gmx.de",
      "userId": "18422164867366802681"
     },
     "user_tz": -120
    },
    "id": "uT_h9S131QBF",
    "outputId": "16203a12-f311-496c-9962-d1d72f5cf290"
   },
   "outputs": [],
   "source": [
    "# prototype system- and user-prompt for synthetic dataset generator\n",
    "dataset_types = [\"Instruction-Response Pairs\", \"Multi-Turn Chat Dialogue\", \"Text Classification\"]\n",
    "response_csv_columns_headers = {\n",
    "    \"Instruction-Response Pairs\": [\"instruction\", \"response\", \"domain\", \"complexity\"],\n",
    "    \"Multi-Turn Chat Dialogue\": [\"conversation_id\", \"turn_number\", \"role\", \"content\"],\n",
    "    \"Text Classification\": [\"text\", \"label\", \"sourcestyle\"],\n",
    "}\n",
    "\n",
    "dataset_type = \"Text Classification\"\n",
    "system_message = f\"You are a dataset generator for {dataset_type}. Respond in csv format only, include the header, nothing extra and use the following columns {response_csv_columns_headers[dataset_type]} .\"\n",
    "\n",
    "target_domain = \"Cooking\"\n",
    "instruction_type = \"Summarization\"\n",
    "diversity_prompt = \"Increase in complexity\"\n",
    "number_of_samples = 5\n",
    "user_message_instruction_response_pairs = f\"For the target domain {target_domain} using the instruction type {instruction_type} create a {dataset_type} dataset. Make the question {diversity_prompt}. Create {number_of_samples} samples.\"\n",
    "\n",
    "scenario_role=\"customer support\"\n",
    "number_of_turns=3\n",
    "conversation_goal=\"Explain a complex concept\"\n",
    "user_message_multi_turn_chat_dialogue = f\"For the scenario role {scenario_role} with {number_of_turns} number of turns and the conversation goal {conversation_goal} create a {dataset_type} dataset. Create {number_of_samples} samples.\"\n",
    "\n",
    "text_type = \"News Headlines\"\n",
    "list_of_labels = [\"Sports\", \"Politics\", \"Tech\"]\n",
    "user_message_text_classification = f\"For the text-type {text_type} and the labels {list_of_labels} create a {dataset_type} dataset. Create {number_of_samples} samples.\"\n",
    "\n",
    "messages = [{\"role\": \"system\", \"content\": system_message}]\n",
    "messages.append({\"role\": \"user\", \"content\": user_message_text_classification})\n",
    "response = generate_stream_with_thread(messages, tokenizer, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 51,
     "status": "ok",
     "timestamp": 1759347628652,
     "user": {
      "displayName": "thinsri@gmx.de",
      "userId": "18422164867366802681"
     },
     "user_tz": -120
    },
    "id": "koJHoRuVDLFN",
    "outputId": "08e302a8-7a57-448c-8c02-9d03218b7343"
   },
   "outputs": [],
   "source": [
    "print(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iObFAoLBwL8F"
   },
   "source": [
    "## 2. Gradio App"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "0GwRK-ecDPj4"
   },
   "outputs": [],
   "source": [
    "dataset_types = [\"Instruction-Response Pairs\", \"Multi-Turn Chat Dialogue\", \"Text Classification\"]\n",
    "response_csv_columns_headers = {\n",
    "    \"Instruction-Response Pairs\": [\"instruction\", \"response\", \"domain\", \"complexity\"],\n",
    "    \"Multi-Turn Chat Dialogue\": [\"conversation_id\", \"turn_number\", \"role\", \"content\"],\n",
    "    \"Text Classification\": [\"text\", \"label\", \"sourcestyle\"],\n",
    "}\n",
    "\n",
    "def update_params(dataset_type):\n",
    "    # Default to hidden for all containers\n",
    "    hide_all = gr.update(visible=False)\n",
    "\n",
    "    ir_update = hide_all\n",
    "    mtd_update = hide_all\n",
    "    tc_update = hide_all\n",
    "\n",
    "    # Set the relevant container to visible based on selection\n",
    "    if dataset_type == \"Instruction-Response Pairs\":\n",
    "        ir_update = gr.update(visible=True)\n",
    "    elif dataset_type == \"Multi-Turn Chat Dialogue\":\n",
    "        mtd_update = gr.update(visible=True)\n",
    "    elif dataset_type == \"Text Classification\":\n",
    "        tc_update = gr.update(visible=True)\n",
    "\n",
    "    # Now, we only return 3 updates!\n",
    "    return ir_update, mtd_update, tc_update\n",
    "\n",
    "def combine_messages(system_message, user_message):\n",
    "  messages = [{\"role\": \"system\", \"content\": system_message}]\n",
    "  messages.append({\"role\": \"user\", \"content\": user_message})\n",
    "  return messages\n",
    "\n",
    "def create_download_link(full_response):\n",
    "  # Create a temporary file\n",
    "  try:\n",
    "    temp_file = tempfile.NamedTemporaryFile(\n",
    "        mode='w',\n",
    "        delete=False,\n",
    "        suffix='.csv',\n",
    "        encoding='utf-8'\n",
    "    )\n",
    "\n",
    "    temp_file.write(full_response)\n",
    "    temp_file.close()\n",
    "    return full_response, gr.update(visible=True, value=temp_file.name)\n",
    "  except Exception as e:\n",
    "    error_message = f\"Error writing to file: {e}\"\n",
    "    return full_response + \"\\n\\n\" + error_message, None\n",
    "\n",
    "def create_download_link_in_memory(full_response):\n",
    "  csv_buffer = io.StringIO(full_response)\n",
    "  csv_buffer.name = \"generated_dataset.csv\"\n",
    "\n",
    "  try:\n",
    "    return full_response, gr.update(visible=True, value=csv_buffer)\n",
    "  except Exception as e:\n",
    "    error_message = f\"Error creating in-memory file: {e}\"\n",
    "    return full_response + \"\\n\\n\" + error_message, gr.update(value=None, visible=False)\n",
    "\n",
    "\n",
    "def create_dataset_ir(dataset_type, target_domain, instruction_type, diversity_prompt, number_of_samples):\n",
    "  global response_csv_columns_headers\n",
    "  system_message = f\"You are a dataset generator for Instruction-Response Pairs. Respond in csv format only, include the header, nothing extra and use the following columns {response_csv_columns_headers[dataset_type]} .\"\n",
    "  user_message = f\"For the target domain {target_domain} using the instruction type {instruction_type} create a {dataset_type} dataset. Make the question {diversity_prompt}. Create {number_of_samples} samples.\"\n",
    "  messages = combine_messages(system_message, user_message)\n",
    "  stream_generator = generate_stream_with_thread_gradio(messages, tokenizer, model)\n",
    "  full_response = \"\"\n",
    "  for update in stream_generator:\n",
    "    full_response = update\n",
    "    yield update, gr.update(visible=False) # Keep download link hidden during stream\n",
    "  # After streaming is complete, create the download file\n",
    "  try:\n",
    "      temp_file = tempfile.NamedTemporaryFile(\n",
    "          mode='w',\n",
    "          delete=False,\n",
    "          suffix='.csv',\n",
    "          encoding='utf-8'\n",
    "      )\n",
    "      temp_file.write(full_response)\n",
    "      temp_file.close()\n",
    "\n",
    "      # Final yield with the complete text and the visible download link\n",
    "      yield full_response, gr.update(visible=True, value=temp_file.name)\n",
    "  except Exception as e:\n",
    "      error_message = f\"Error writing to file: {e}\"\n",
    "      yield full_response + \"\\n\\n\" + error_message, gr.update(visible=False)\n",
    "\n",
    "\n",
    "def create_dataset_mtd(dataset_type, scenario_role, number_of_turns, conversation_goal, number_of_samples):\n",
    "  global response_csv_columns_headers\n",
    "  system_message = f\"You are a dataset generator for Multi-Turn Chat Dialogue. Respond in csv format only, include the header, nothing extra and use the following columns {response_csv_columns_headers[dataset_type]} .\"\n",
    "  user_message = f\"For the scenario role {scenario_role} with {number_of_turns} number of turns and the conversation goal {conversation_goal} create a {dataset_type} dataset. Create {number_of_samples} samples.\"\n",
    "  messages = combine_messages(system_message, user_message)\n",
    "  stream_generator = generate_stream_with_thread_gradio(messages, tokenizer, model)\n",
    "  full_response = \"\"\n",
    "  for update in stream_generator:\n",
    "    full_response = update\n",
    "    yield update, gr.update(visible=False) # Keep download link hidden during stream\n",
    "  try:\n",
    "      temp_file = tempfile.NamedTemporaryFile(\n",
    "          mode='w',\n",
    "          delete=False,\n",
    "          suffix='.csv',\n",
    "          encoding='utf-8'\n",
    "      )\n",
    "      temp_file.write(full_response)\n",
    "      temp_file.close()\n",
    "\n",
    "      # Final yield with the complete text and the visible download link\n",
    "      yield full_response, gr.update(visible=True, value=temp_file.name)\n",
    "  except Exception as e:\n",
    "      error_message = f\"Error writing to file: {e}\"\n",
    "      yield full_response + \"\\n\\n\" + error_message, gr.update(visible=False)\n",
    "\n",
    "def create_dataset_tc(dataset_type, text_type, list_of_labels, number_of_samples):\n",
    "  global response_csv_columns_headers\n",
    "  system_message = f\"You are a dataset generator for Text Classification. Respond in csv format only, include the header, nothing extra and use the following columns {response_csv_columns_headers[dataset_type]} .\"\n",
    "  user_message = f\"For the text-type {text_type} and the labels {list_of_labels} create a {dataset_type} dataset. Create {number_of_samples} samples.\"\n",
    "  messages = combine_messages(system_message, user_message)\n",
    "  stream_generator = generate_stream_with_thread_gradio(messages, tokenizer, model)\n",
    "  full_response = \"\"\n",
    "  for update in stream_generator:\n",
    "    full_response = update\n",
    "    yield update, gr.update(visible=False) # Keep download link hidden during stream\n",
    "  try:\n",
    "      temp_file = tempfile.NamedTemporaryFile(\n",
    "          mode='w',\n",
    "          delete=False,\n",
    "          suffix='.csv',\n",
    "          encoding='utf-8'\n",
    "      )\n",
    "      temp_file.write(full_response)\n",
    "      temp_file.close()\n",
    "\n",
    "      # Final yield with the complete text and the visible download link\n",
    "      yield full_response, gr.update(visible=True, value=temp_file.name)\n",
    "  except Exception as e:\n",
    "      error_message = f\"Error writing to file: {e}\"\n",
    "      yield full_response + \"\\n\\n\" + error_message, gr.update(visible=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 680
    },
    "executionInfo": {
     "elapsed": 82700,
     "status": "ok",
     "timestamp": 1759468954578,
     "user": {
      "displayName": "thinsri@gmx.de",
      "userId": "18422164867366802681"
     },
     "user_tz": -120
    },
    "id": "lqrDsgEiwL_n",
    "outputId": "81e41895-7c64-44ad-b05f-9b4b92030389"
   },
   "outputs": [],
   "source": [
    "import gradio as gr\n",
    "\n",
    "with gr.Blocks() as demo:\n",
    "  # 1. Main Selection\n",
    "  with gr.Row():\n",
    "    with gr.Column():\n",
    "      dataset_type = gr.Dropdown(\n",
    "          choices=dataset_types,\n",
    "          label=\"Dataset Type\",\n",
    "          value=None,\n",
    "          )\n",
    "\n",
    "  with gr.Column(visible=False) as ir_container:\n",
    "    with gr.Row():\n",
    "      target_domain = gr.Textbox(label=\"Target Domain\")\n",
    "      instruction_type = gr.Textbox(label=\"Instruction Type\")\n",
    "      diversity_prompt = gr.Textbox(label=\"Diversity Prompt\")\n",
    "      number_of_samples_ir = gr.Textbox(label=\"Number of Samples\")\n",
    "    with gr.Row():\n",
    "      submit_ir = gr.Button(\"Submit\")\n",
    "\n",
    "    ir_output = gr.Textbox(label=\"AI Response\", lines=10)\n",
    "    ir_download_link = gr.File(label=\"Download Generated Dataset (.csv)\", visible=False)\n",
    "\n",
    "  with gr.Column(visible=False) as mtd_container:\n",
    "    with gr.Row():\n",
    "      scenario_role = gr.Textbox(label=\"Scenario Role\")\n",
    "      number_of_turns = gr.Textbox(label=\"Number of Turns\")\n",
    "      conversation_goal = gr.Textbox(label=\"Conversation Goal\")\n",
    "      number_of_samples_mtd = gr.Textbox(label=\"Number of Samples\")\n",
    "    with gr.Row():\n",
    "      submit_mtd = gr.Button(\"Submit\")\n",
    "\n",
    "    mtd_output = gr.Textbox(label=\"AI Response\", lines=10)\n",
    "    mtd_download_link = gr.File(label=\"Download Generated Dataset (.csv)\", visible=False)\n",
    "\n",
    "  with gr.Column(visible=False) as tc_container:\n",
    "    with gr.Row():\n",
    "      text_type = gr.Textbox(label=\"Text Type\")\n",
    "      list_of_labels = gr.Textbox(label=\"List of Labels\")\n",
    "      number_of_samples_tc = gr.Textbox(label=\"Number of Samples\")\n",
    "    with gr.Row():\n",
    "      submit_tc = gr.Button(\"Submit\")\n",
    "\n",
    "    tc_output = gr.Textbox(label=\"AI Response\", lines=10)\n",
    "    tc_download_link = gr.File(label=\"Download Generated Dataset (.csv)\", visible=False)\n",
    "\n",
    "  # Attach the listener\n",
    "  dataset_type.change(update_params, inputs=[dataset_type], outputs=[ir_container, mtd_container, tc_container])\n",
    "  submit_ir.click(create_dataset_ir, inputs=[dataset_type, target_domain, instruction_type, diversity_prompt, number_of_samples_ir], outputs=[ir_output, ir_download_link])\n",
    "  submit_mtd.click(create_dataset_mtd, inputs=[dataset_type, scenario_role, number_of_turns, conversation_goal, number_of_samples_mtd], outputs=[mtd_output, mtd_download_link])\n",
    "  submit_tc.click(create_dataset_tc, inputs=[dataset_type, text_type, list_of_labels, number_of_samples_tc], outputs=[tc_output, tc_download_link])\n",
    "\n",
    "\n",
    "demo.launch(debug=True, share=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QnbU-q7d2oGB"
   },
   "source": [
    "## Refactored Code and named temp-file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 680
    },
    "executionInfo": {
     "elapsed": 75622,
     "status": "ok",
     "timestamp": 1759636078533,
     "user": {
      "displayName": "thinsri@gmx.de",
      "userId": "18422164867366802681"
     },
     "user_tz": -120
    },
    "id": "9sSdP7hy2oLt",
    "outputId": "d99aea8e-feeb-4c9d-d2ee-4d090b295128"
   },
   "outputs": [],
   "source": [
    "import tempfile\n",
    "import os\n",
    "import gradio as gr\n",
    "\n",
    "dataset_types = [\"Instruction-Response Pairs\", \"Multi-Turn Chat Dialogue\", \"Text Classification\"]\n",
    "\n",
    "response_csv_columns_headers = {\n",
    "    \"Instruction-Response Pairs\": [\"instruction\", \"response\", \"domain\", \"complexity\"],\n",
    "    \"Multi-Turn Chat Dialogue\": [\"conversation_id\", \"turn_number\", \"role\", \"content\"],\n",
    "    \"Text Classification\": [\"text\", \"label\", \"sourcestyle\"],\n",
    "}\n",
    "\n",
    "# System message templates for each dataset type\n",
    "system_message_templates = {\n",
    "    \"Instruction-Response Pairs\": \"You are a dataset generator for Instruction-Response Pairs. Respond in csv format only, include the header, nothing extra and use the following columns {columns}.\",\n",
    "    \"Multi-Turn Chat Dialogue\": \"You are a dataset generator for Multi-Turn Chat Dialogue. Respond in csv format only, include the header, nothing extra and use the following columns {columns}.\",\n",
    "    \"Text Classification\": \"You are a dataset generator for Text Classification. Respond in csv format only, include the header, nothing extra and use the following columns {columns}.\",\n",
    "}\n",
    "\n",
    "# User message templates for each dataset type\n",
    "user_message_templates = {\n",
    "    \"Instruction-Response Pairs\": \"For the target domain {target_domain} using the instruction type {instruction_type} create a {dataset_type} dataset. Make the question {diversity_prompt}. Create {number_of_samples} samples.\",\n",
    "    \"Multi-Turn Chat Dialogue\": \"For the scenario role {scenario_role} with {number_of_turns} number of turns and the conversation goal {conversation_goal} create a {dataset_type} dataset. Create {number_of_samples} samples.\",\n",
    "    \"Text Classification\": \"For the text-type {text_type} and the labels {list_of_labels} create a {dataset_type} dataset. Create {number_of_samples} samples.\",\n",
    "}\n",
    "\n",
    "def update_params(dataset_type):\n",
    "    \"\"\"Update visibility of parameter containers based on selected dataset type\"\"\"\n",
    "    hide_all = gr.update(visible=False)\n",
    "\n",
    "    visibility_map = {\n",
    "        \"Instruction-Response Pairs\": (gr.update(visible=True), hide_all, hide_all),\n",
    "        \"Multi-Turn Chat Dialogue\": (hide_all, gr.update(visible=True), hide_all),\n",
    "        \"Text Classification\": (hide_all, hide_all, gr.update(visible=True)),\n",
    "    }\n",
    "\n",
    "    return visibility_map.get(dataset_type, (hide_all, hide_all, hide_all))\n",
    "\n",
    "def combine_messages(system_message, user_message):\n",
    "    \"\"\"Combine system and user messages into a message list\"\"\"\n",
    "    return [\n",
    "        {\"role\": \"system\", \"content\": system_message},\n",
    "        {\"role\": \"user\", \"content\": user_message}\n",
    "    ]\n",
    "\n",
    "def create_dataset(dataset_type, **kwargs):\n",
    "    \"\"\"\n",
    "    Generic dataset creation function that handles all dataset types\n",
    "\n",
    "    Args:\n",
    "        dataset_type: Type of dataset to generate\n",
    "        **kwargs: Dynamic parameters based on dataset type\n",
    "    \"\"\"\n",
    "    # Get the appropriate columns for this dataset type\n",
    "    columns = response_csv_columns_headers[dataset_type]\n",
    "\n",
    "    # Create system message\n",
    "    system_message = system_message_templates[dataset_type].format(columns=columns)\n",
    "\n",
    "    # Create user message with appropriate template and parameters\n",
    "    user_message = user_message_templates[dataset_type].format(\n",
    "        dataset_type=dataset_type,\n",
    "        **kwargs\n",
    "    )\n",
    "\n",
    "    # Combine messages\n",
    "    messages = combine_messages(system_message, user_message)\n",
    "\n",
    "    # Generate stream\n",
    "    stream_generator = generate_stream_with_thread_gradio(messages, tokenizer, model)\n",
    "    full_response = \"\"\n",
    "\n",
    "    # Stream the text output\n",
    "    for update in stream_generator:\n",
    "        full_response = update\n",
    "        yield update, gr.update(visible=False)  # Keep download hidden during stream\n",
    "\n",
    "    # After streaming is complete, create the download file\n",
    "    try:\n",
    "        temp_filename = f\"generated_dataset_{dataset_type}.csv\"\n",
    "        temp_dir = tempfile.gettempdir()\n",
    "        temp_filepath = os.path.join(temp_dir, temp_filename)\n",
    "\n",
    "        with open(temp_filepath, 'w', encoding='utf-8') as temp_file:\n",
    "            temp_file.write(full_response)\n",
    "\n",
    "        # Final yield with the complete text and the visible download link\n",
    "        yield full_response, gr.update(visible=True, value=temp_file.name)\n",
    "    except Exception as e:\n",
    "        error_message = f\"Error writing to file: {e}\"\n",
    "        yield full_response + \"\\n\\n\" + error_message, gr.update(visible=False)\n",
    "\n",
    "# Wrapper functions for each dataset type (to handle different parameter names)\n",
    "def create_dataset_ir(dataset_type, target_domain, instruction_type, diversity_prompt, number_of_samples):\n",
    "    \"\"\"Create Instruction-Response Pairs dataset\"\"\"\n",
    "    yield from create_dataset(\n",
    "        dataset_type,\n",
    "        target_domain=target_domain,\n",
    "        instruction_type=instruction_type,\n",
    "        diversity_prompt=diversity_prompt,\n",
    "        number_of_samples=number_of_samples\n",
    "    )\n",
    "\n",
    "def create_dataset_mtd(dataset_type, scenario_role, number_of_turns, conversation_goal, number_of_samples):\n",
    "    \"\"\"Create Multi-Turn Chat Dialogue dataset\"\"\"\n",
    "    yield from create_dataset(\n",
    "        dataset_type,\n",
    "        scenario_role=scenario_role,\n",
    "        number_of_turns=number_of_turns,\n",
    "        conversation_goal=conversation_goal,\n",
    "        number_of_samples=number_of_samples\n",
    "    )\n",
    "\n",
    "def create_dataset_tc(dataset_type, text_type, list_of_labels, number_of_samples):\n",
    "    \"\"\"Create Text Classification dataset\"\"\"\n",
    "    yield from create_dataset(\n",
    "        dataset_type,\n",
    "        text_type=text_type,\n",
    "        list_of_labels=list_of_labels,\n",
    "        number_of_samples=number_of_samples\n",
    "    )\n",
    "\n",
    "# Gradio UI\n",
    "with gr.Blocks() as demo:\n",
    "    gr.Markdown(\"# Synthetic Dataset Generator\")\n",
    "\n",
    "    # Main Selection\n",
    "    with gr.Row():\n",
    "        with gr.Column():\n",
    "            dataset_type = gr.Dropdown(\n",
    "                choices=dataset_types,\n",
    "                label=\"Dataset Type\",\n",
    "                value=None,\n",
    "            )\n",
    "\n",
    "    # Instruction-Response Pairs Container\n",
    "    with gr.Column(visible=False) as ir_container:\n",
    "        gr.Markdown(\"### Instruction-Response Pairs Parameters\")\n",
    "        with gr.Row():\n",
    "            target_domain = gr.Textbox(label=\"Target Domain\")\n",
    "            instruction_type = gr.Textbox(label=\"Instruction Type\")\n",
    "            diversity_prompt = gr.Textbox(label=\"Diversity Prompt\")\n",
    "            number_of_samples_ir = gr.Textbox(label=\"Number of Samples\")\n",
    "        with gr.Row():\n",
    "            submit_ir = gr.Button(\"Generate Dataset\")\n",
    "\n",
    "        ir_output = gr.Textbox(label=\"AI Response\", lines=10)\n",
    "        ir_download_link = gr.File(label=\"Download Generated Dataset (.csv)\", visible=False)\n",
    "\n",
    "    # Multi-Turn Chat Dialogue Container\n",
    "    with gr.Column(visible=False) as mtd_container:\n",
    "        gr.Markdown(\"### Multi-Turn Chat Dialogue Parameters\")\n",
    "        with gr.Row():\n",
    "            scenario_role = gr.Textbox(label=\"Scenario Role\")\n",
    "            number_of_turns = gr.Textbox(label=\"Number of Turns\")\n",
    "            conversation_goal = gr.Textbox(label=\"Conversation Goal\")\n",
    "            number_of_samples_mtd = gr.Textbox(label=\"Number of Samples\")\n",
    "        with gr.Row():\n",
    "            submit_mtd = gr.Button(\"Generate Dataset\")\n",
    "\n",
    "        mtd_output = gr.Textbox(label=\"AI Response\", lines=10)\n",
    "        mtd_download_link = gr.File(label=\"Download Generated Dataset (.csv)\", visible=False)\n",
    "\n",
    "    # Text Classification Container\n",
    "    with gr.Column(visible=False) as tc_container:\n",
    "        gr.Markdown(\"### Text Classification Parameters\")\n",
    "        with gr.Row():\n",
    "            text_type = gr.Textbox(label=\"Text Type\")\n",
    "            list_of_labels = gr.Textbox(label=\"List of Labels\")\n",
    "            number_of_samples_tc = gr.Textbox(label=\"Number of Samples\")\n",
    "        with gr.Row():\n",
    "            submit_tc = gr.Button(\"Generate Dataset\")\n",
    "\n",
    "        tc_output = gr.Textbox(label=\"AI Response\", lines=10)\n",
    "        tc_download_link = gr.File(label=\"Download Generated Dataset (.csv)\", visible=False)\n",
    "\n",
    "    # Event Handlers\n",
    "    dataset_type.change(\n",
    "        update_params,\n",
    "        inputs=[dataset_type],\n",
    "        outputs=[ir_container, mtd_container, tc_container]\n",
    "    )\n",
    "\n",
    "    submit_ir.click(\n",
    "        create_dataset_ir,\n",
    "        inputs=[dataset_type, target_domain, instruction_type, diversity_prompt, number_of_samples_ir],\n",
    "        outputs=[ir_output, ir_download_link],\n",
    "    )\n",
    "\n",
    "    submit_mtd.click(\n",
    "        create_dataset_mtd,\n",
    "        inputs=[dataset_type, scenario_role, number_of_turns, conversation_goal, number_of_samples_mtd],\n",
    "        outputs=[mtd_output, mtd_download_link],\n",
    "    )\n",
    "\n",
    "    submit_tc.click(\n",
    "        create_dataset_tc,\n",
    "        inputs=[dataset_type, text_type, list_of_labels, number_of_samples_tc],\n",
    "        outputs=[tc_output, tc_download_link],\n",
    "    )\n",
    "\n",
    "# Launch with share=True for Google Colab\n",
    "demo.launch(debug=True, share=True)"
   ]
  }
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